Overview
- Discusses how to analyze mathematically imprecise, uncertain, fuzzy information
- Shows how to construct input data for use in flexible and generalized uncertainty optimization problems
- Second edition enriched with more examples and a chapter on interval multi-objective mini-max regret theory
Part of the book series: Studies in Computational Intelligence (SCI, volume 696)
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Table of contents (6 chapters)
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Front Matter
Keywords
About this book
This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.
Authors and Affiliations
About the authors
Luiz L. Salles-Neto received the M.Sc. degree in mathematics and the Ph.D. degree in computational and applied mathematics from the University of Campinas, Brazil, in 2000 and 2005, respectively. He was a Research Scholar at the Universidad de Sevilla, Spain, in 2009/2010, and a Research Scholar at the University of Colorado Denver, USA, in 2017. He is an Associate Professor at Federal University of São Paulo, Brazil.
Bibliographic Information
Book Title: Flexible and Generalized Uncertainty Optimization
Book Subtitle: Theory and Approaches
Authors: Weldon A. Lodwick, Luiz L. Salles-Neto
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-61180-4
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-61179-8Published: 13 January 2021
Softcover ISBN: 978-3-030-61182-8Published: 13 January 2022
eBook ISBN: 978-3-030-61180-4Published: 12 January 2021
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 2
Number of Pages: IX, 193
Number of Illustrations: 4 b/w illustrations, 30 illustrations in colour
Topics: Computational Intelligence, Optimization, Operations Research/Decision Theory, Probability Theory and Stochastic Processes